Question 1

Is this machine learning course completely free of cost?

Accepted Answer

Yes, this machine learning free certification is completely free. You'll access all course materials, projects, and receive your certificate without any payment required.

Question 2

What are the prerequisites to enroll in this free machine learning course?

Accepted Answer

Basic Python knowledge is helpful but not required. The course starts with fundamentals and builds up gradually, making it accessible to beginners with any programming background.

Question 3

Is this machine learning course suitable for beginners?

Accepted Answer

Absolutely. This free online machine learning course is designed for beginners, with clear explanations and practical examples that make complex concepts easy to understand.

Question 4

What is the format of this free machine learning certification?

Accepted Answer

The course combines video lessons, hands-on coding exercises, and practical projects to provide a comprehensive learning experience. You'll learn through doing, with immediate feedback on your progress throughout the program.

Question 5

What specific machine learning skills will I learn in this free course?

Accepted Answer

You'll master supervised learning, unsupervised learning, regression, classification, clustering, ensemble methods, and deep learning fundamentals using Python and popular ML libraries.

Question 6

What are the topics covered in this free machine learning course with a certificate?

Accepted Answer

Key topics include linear/logistic regression, SVM, decision trees, random forests, K-means clustering, Naive Bayes, boosting/bagging techniques, time series modeling, and neural network basics.

Question 7

Is the course content updated with the latest machine learning features?

Accepted Answer

Yes, the course content reflects current industry practices and includes recent developments in machine learning algorithms and Python libraries commonly used in professional settings.

Question 8

What real-world applications of machine learning are covered in this course?

Accepted Answer

You'll explore applications in finance (fraud detection), healthcare (diagnosis prediction), marketing (customer segmentation), e-commerce (recommendation systems), and more across various industries.

Question 9

How can learning a machine learning course benefit my career?

Accepted Answer

ML skills are in high demand across industries. This certification can lead to roles in data science, AI engineering, business analytics, and consulting with competitive salaries and growth opportunities.

Question 10

What types of professionals should take this free machine learning course with a certificate?

Accepted Answer

Developers, analysts, business professionals, students, and anyone interested in data-driven problem solving will benefit from this comprehensive introduction to machine learning.

Question 11

Are machine learning skills in high demand in the job market?

Accepted Answer

Yes, ML skills are among the most sought-after technical competencies. Companies across all sectors are investing in data science and AI, creating abundant opportunities for trained professionals.

Question 12

How does this machine learning certification help me in my career?

Accepted Answer

The certificate validates your ML knowledge to employers, demonstrates your commitment to learning new technologies, and provides concrete proof of your technical capabilities in this growing field.

Question 13

Will I receive a certificate after completing this machine learning course?

Accepted Answer

Yes, you'll receive an official completion certificate that you can add to your resume, LinkedIn profile, or professional portfolio to showcase your machine learning expertise.

Question 14

What will be my next steps after completing this free machine learning course?

Accepted Answer

Consider advancing to specialized areas, such as deep learning, natural language processing, or computer vision. You might also pursue advanced certifications or apply your skills in real-world projects and internships.

Question 15

What are the other best free Machine Learning courses on SkillUP?

Accepted Answer

SkillUP offers many free certification courses online, including Python for Data Science, Data Analytics, and AI fundamentals. These free online courses complement your ML knowledge and expand your data science skill set.

Free Machine Learning Course Online with Certificate

Machine Learning Skills you will learn

Who should learn this free Machine Learning course?

What you will learn in this free Machine Learning course?

Machine Learning using Python

Lesson 01 - Course Introduction

1.01 Course Introduction

1.02 What You Will Learn

Lesson 02 - Introduction to Machine Learning

2.01 Introduction

2.02 What Is Machine Learning?

2.03 Types of Machine Learning

2.04 Machine Learning Pipeline and MLOP's

2.05 Introduction to Python Packages Used in Machine Learning

2.06 Recap

Lesson 03 - Supervised Learning

3.01 Introduction

3.02 Supervised Learning

3.03 Applications of Supervised Learning

3.04 Preparing and Shaping Data

3.05 What is overfitting and underfitting?

3.06 Detecting and Preventing Overfitting and Underfitting

3.07 Regularization

3.08 Recap

Lesson 04 - Regression and Applications

4.01 Introduction

4.02 What is Regression?

4.03 Regression Types: Introduction

4.04 Linear Regression

4.05 Working with Linear Regression

4.06 Critical Assumptions for Linear Regression

4.07 Logistic Regression

4.08 Data Exploration Using SMOTE

4.09 Over Sampling Using SMOTE

4.10 Polynomial Regression

4.11 Data Preparation Model Building and Performance Evaluation Part A

4.12 Ridge Regression

4.13 Data Preparation Model Building: Part B

4.14 LASSO Regression

4.15 Data Preparation Model Building: Part C

4.16 Recap

4.17 Spotlight

Lesson 05 - Classification and Applications

5.01 Introduction

5.02 What are Classification Algorithms?

5.03 Types of Classification

5.04 Types and selection of performance parameters

5.05 Naive Bayes

5.06 Applying Naive Bayes Classifier

5.07 Stochastic Gradient Descent

5.08 Applying Stochastic Gradient Descent

5.09 K Nearest Neighbors

5.10 Applying K Nearest Neighbors

5.11 Decision Tree

5.12 Applying Decision Tree

5.13 Random Forest

5.14 Applying Random Forest

5.15 Boruta Explained

5.16 Automatic Feature Selection with Boruta

5.17 Support Vector Machine

5.18 Applying Support Vector Machine

5.19 Cohens Kappa Measure

5.20 Recap

Lesson 06 - Unsupervised Algorithms

6.01 Introduction

6.02 What are Unsupervised Algorithms?

6.03 Types of Unsupervised Algorithms Clustering and Associative

6.04 When to Use Unsupervised Algorithms?

6.05 Visualizing Outputs

6.06 Performance Parameters

6.07 Clustering Types

6.08 Hierarchical Clustering

6.09 Applying Hierarchical Clustering

6.10 K means Clustering: Part 1

6.11 K means Clustering: Part 2

6.12 Applying K Means Clustering

6.13 KNN-K Nearest Neighbors

6.14 Outlier Detection

6.15 Outlier Detection Algorithms in PyOD

6.16 Demo: K NN for Anomaly Detection